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Use data to attack declining reimbursements

If being proactive is the name of the game, then clean, accurate and current data is the main player

Chief financial officers at hospitals and health systems must track a multitude of difficult financial issues these days, including the repercussions from Meaningful Use and the still-looming ICD-10 implementation. One question, however, always stops a conversation cold: Why are our reimbursements declining?

As important as it is for CFOs to keep their eyes on an issue like Meaningful Use, they simply cannot afford to be blindsided by declining reimbursements or denied claims. Indeed, unless they can flush out the reasons for a decline or denial, and quickly formulate a plan to combat it, some CFOs will be out of a job.

In today’s healthcare environment, several factors typically contribute to overall reimbursement declines. On the payer side, significant Medicare and Medicaid fee schedule reductions have been accompanied by lower commercial plan rates. Provisions of the Affordable Care Act (ACA) also must be taken into consideration. Readmission penalties, for instance, can cost hospitals as much as a three percent fee adjustment.

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Time is perhaps a CFO’s biggest enemy. Discovering significant reimbursement declines and payment denials several months in—regardless of cause—can be crippling. Finance executives who rely on reports and spreadsheets that analyze days-old or even weeks-old data may have lost the reimbursement match without even realizing it.

What is the right data?
If being proactive is the name of the game, then clean, accurate and current data is the main player. It is no longer enough just to have the “right” data; CFOs must be able to glean actionable intelligence out of constantly changing information. At the very least, they must have direct access to:

  • financial data, such as labor costs, materials costs, accounts receivable (A/R) levels, and denial rates;
  • clinical data, such as readmission rates and lengths of stay; and
  • administrative data, such as referral patterns, patient satisfaction scores, costs of service, and profitability levels by service line, by practitioner.

Yet data that is right today may not be right tomorrow, and more information is not necessarily better information. Some hospital and health system leaders are beginning to experience the drawbacks of unwieldy data warehouses that require them to submit queries and wait days or weeks for someone to deliver the results. Worse still: Sometimes they carefully formulate a query, await the results, and then realize after the fact that the requested report fails to tell them what they really need to know.

To facilitate data-driven decision making, every key piece of information should be directly accessible to CFOs, department leaders and other executives. Ideally, everyone in the C-suite should have a custom dashboard that provides access to critical key performance indicators whenever they need it.

The ability to combine financial, clinical and administrative data is another essential element, especially for executives aiming to change clinician behavior. Improving clinical documentation, for example, is usually an uphill battle; that is, until providers are shown the precise amount of money they are losing every week due to documentation shortfalls.

The type of predictive data analysis only possible with a fully integrated business intelligence solution gives CFOs and other executives the ability to peer into the root causes of reimbursement declines. Combining financial, clinical and administrative data and using it to derive predictive intelligence can help answer questions such as:

  • How much of the organization’s commercial revenue is being written off as bad debt?
  • Should the group recruit more orthopedic surgeons or more cardiac surgeons to improve the bottom line?
  • Which area physicians should be referring to the hospital, but aren’t?
  • Which lines of service should be marketed for the highest return on investment?
  • Which patient experience factors have the greatest effect on ratings?
  • What factors contributed to last month’s rise in A/R levels?
  • What would a three percent reimbursement decline mean for the overall financial health of the organization over the next two quarters?

Data and predictive analytics
CFOs who feel like the rules have changed in the middle of the game are not alone. Understanding the general reimbursement landscape is not enough, nor can executives run their organizations on reports filled with months-old data for right now decisions.

Traditional revenue cycle questions remain valid, but new questions about the relationships between costs and care quality also must be answered quickly, effectively and efficiently.

With a strong base of comprehensive data and advanced predictive analytics, CFOs can turn their financial ship on a dime. From revamping lines of service to bolstering referral sources or strengthening the revenue cycle, predictive analytics can help drive the decisions that quickly reverse—or even prevent—reimbursement declines and payment denials.